Python OpenCV 图像矫正的原理实现

correct('images/1.jpeg', 180, 260)
correct('images/2.jpeg', 30, 100)
correct('images/3.jpeg', 100, 160)

def correct(image_path, threshold1, threshold2):
    # 读取图像并转换为灰度图像
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # 使用Canny算子检测边缘
    edges = canny_detect(gray, threshold1, threshold2, show=False)
    # 使用Hough检测直线
    lines = hough_detect(image, edges, show=False)
    # 手动筛选
    for id, line in enumerate(lines):
        rho, theta = line[0]
        x1, y1, x2, y2 = convert_polar_to_two_points(rho, theta)
        temp_image = image.copy()
        cv2.line(temp_image, (x1, y1), (x2, y2), (255, 0, 0), 7)
        plt.subplot(5, 5, id + 1)
        plt.imshow(temp_image)
        plt.title('{}'.format(id))
        plt.xticks([])
        plt.yticks([])
    plt.show()
    choose = input('请输入您选择的直线的id，以空格分隔：').split(' ')
 
    # 求解交点
    crossover_points = []
    assert len(choose) == 4
    for i in range(4):
        for j in range(i+1, 4):
            rho1, theta1 = lines[int(choose[i])][0]
            rho2, theta2 = lines[int(choose[j])][0]
            # 如果角度差太小，认为它们是平行线
            if abs(theta2 - theta1) > np.pi / 8 and abs(theta2 - theta1) < np.pi * 7 / 8:
                crossover_points.append(cal_crossover(rho1, theta1, rho2, theta2))
    # 确定变换前后的坐标
    before = np.float32(crossover_points)
    after = np.float32([[0, 0], [800, 0], [0, 600], [800, 600]])
    # 单应变换
    h = cv2.getPerspectiveTransform(before, after)
    result = cv2.warpPerspective(image, h, (800, 600))
    cv2.imwrite(image_path.split('.')[0] + '_correct.jpeg', result)
    return result